Looking forward, although Paulihedral is designed from an algorithmic perspective, it can incorporate those technology-driven optimizations.
For example, our technology-dependent passes can be further optimized with more comprehensive models of the target devices. Paulihedral can be extended to other technologies
(e.g., ion trap [1]}, [2]}, photonics [3]}) by adding new passes.
It is also possible to make Paulihedral more intelligent by automatically managing the passes based on the input program characteristics.
We have already observed that the different Pauli string patterns can affect the final improvement under different pass configurations as discussed in Section .
How to tune the pass algorithms or derive new suitable passes base on the simulation kernel characteristics
is worth exploring.
Finally, we believe that the idea of high-level optimization
can be extended to other quantum algorithm design techniques (e.g., phase estimation [4]}, amplitude amplification [5]}) and promising quantum application domains (e.g., quantum machine learning [6]}).
